一种面向情境的情境感知应用编程框架

随着信息技术的快速发展,各种各样的环境信息影响着我们生活中的各种决策,因此提供一种智能化的应用程序,能够根据用户当前的情境信息为用户提供所需要的最适合的服务变得越来越重要。许多遗产软件并不支持情境感知功能,目前的一些主要的编程语言和编程框架并没有提供一个很好的方式来支持这种情境感知应用程序的开发。提出了一个新的面向情境的编程框架,支持情境信息的定义、封装、发布,支持对应用行为的驱动和约束,允许应用快速响应非预期情境。通过该框架可以快速开发情境感知应用,也可以以非侵入的方式改造遗产软件,使之具有情境感知能力。     

The ECORA framework: A hybrid architecture for context-oriented pervasive computing

An infrastructure approach to support context-aware pervasive computing is advantageous for rapid prototyping of context-aware distributed applications and beneficial for unifying modelling of context and reasoning in uncertain conditions. This paper presents the ECORA framework for context-aware computing, which is designed with a focus on reasoning about context under uncertainty and addressing issues of heterogeneity, scalability, communication and usability. The framework follows an agent-oriented hybrid approach, combining centralized reasoning services with context-aware, reasoning capable mobile software agents. The use of a centralized reasoning engine provides powerful reasoning capabilities and deploying context-aware mobile agents enables agility and robustness of components in the pervasive system. The design and implementation of the framework at different levels, as well as three case studies, are presented.     

iConAwa - An intelligent context-aware system

Context-awareness becomes an increasingly important concept in the development of mobile and ubiquitous systems. Applications and services, which run in these kinds of highly dynamic environments, should be aware of and adapt to their contexts. Context-aware applications improve and enrich people’s interactions with devices, computers and other people.In this paper, design and development of iConAwa, which is an intelligent context-aware multi-agent system proactively providing mobile users with context-aware information and services, is described. In iConAwa, mobile users can get information and services about nearby resources (attraction points) according to their context and also communicate with each other by exchanging messages. Context and point of interest ontologies are developed in OWL. Context and points of interest are modelled in a flexible and extensible way by the developed ontology models. Knowledge sharing and knowledge reuse are also provided by using these ontology models. iConAwa makes use of rule-based context reasoning which provides derivation of high level implicit context from low level explicit context. With this approach context reasoning is decoupled from the source code of the system. JADE agent development framework is used to develop the agents and Jena semantic web framework is used to manipulate ontologies and for rule based reasoning.     

Supporting pervasive computing applications with active context fusion and semantic context delivery

Future pervasive computing applications are envisioned to adapt the applications’ behaviors by utilizing various contexts of an environment and its users. Such context information may often be ambiguous and also heterogeneous, which make the delivery of unambiguous context information to real applications extremely challenging. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this paper, we propose a resource optimized quality assured context mediation framework based on efficient context-aware data fusion and semantic-based context delivery. In this framework, contexts are first fused by an active fusion technique based on Dynamic Bayesian Networks and ontology, and further mediated using a composable ontological rule-based model with the involvement of users or application developers. The fused context data are then organized into an ontology-based semantic network together with the associated ontologies in order to facilitate efficient context delivery. Experimental results using SunSPOT and other sensors demonstrate the promise of this approach.     

JCOOLS: A toolkit for generating context-aware applications with JCAF and DROOLS

We present a toolkit named JCOOLS that effectively generates context-aware applications in a ubiquitous environment. With JCOOLS, developers can define contexts and actions as context rules according to the change of context information. Based on the predefined context rules and the underlying DROOLS inference engine, JCOOLS generates responding actions that would execute in the associated end-user applications. In addition, to facilitate the development and deployment of context-aware applications, JCOOLS generates abstract program codes based on the context information for JCAF.     

状态自动机支持的环境感知应用框架研究

环境感知是普及计算中一个重要的研究领域。为了便于开发环境感知应用程序，许多研究者已经提出了一些环境感知应用框架。然而，目前的一些环境感知应用框架侧重于让应用程序直接订阅或查询相关的环境信息，使得开发者需要关注众多的环境信息且要定义许多相似的规则来区分众多不同的操作，造成了开发和修改环境感知的应用程序仍存在着较多的困难。为解决这些问题，本文在研究环境感知应用框架的基础上，引入了信息空间环境状态的概念，提出了一种基于状态自动机的环境感知改进框架BS-CTK，并设计和实现了相应的组件以支持基于状态自动机的环境感知应用开发。     

Hybreed: A software framework for developing context-aware hybrid recommender systems

This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed’s functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.     

Triggering information by context

With the increased availability of personal computers with attached sensors to capture their environment, there is a big opportunity forcontext-aware applications; these automatically provide information and/or take actions according to the user's present context, as detected by sensors. When well designed, these applications provide an opportunity to tailor the provision of information closely to the user's current needs. A sub-set of context-aware applications arediscrete applications, where discrete pieces of information are attached to individual contexts, to be triggered when the user enters those contexts. The advantage of discrete applications is that authoring them can be solely a creative process rather than a programming process: it can be a task akin to creating simple web pages. This paper looks at a general system that can be used in any discrete context-aware application. It propounds a general triggering rule, and investigates how this rule applies in practical applications.     

Design and validation of a light inference system to support embedded context reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.     

A comprehensive approach to model and use context for adapting applications in pervasive environments

With an increasing diversity of pervasive computing devices integrated in our surroundings and an increasing mobility of users, it will be important for computer systems and applications to be context-aware. Lots of works have already been done in this direction on how to capture context data and how to carry it to the application. Among the remaining challenges are to create the intelligence to analyze the context information and deduce the meaning out of it, and to integrate it into adaptable applications. Our work focuses on these challenges by defining generic context storage and processing model and by studying its impact on the application core. We propose a reusable context ontology model that is based on two levels: a generic level and a domain specific level. We propose a generic adaptation framework to guarantee adaptation of applications to the context in a pervasive computing environment. We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications. Our case study shows that the context model and the application adaptation strategies provide promising service architecture.     

一个情境相关的双层室内空间数据模型

在室内空间的移动对象管理中,如何建立数据模型是要解决的首要问题。随着情境感知信息系统的发展,情境的概念得到越来越多的关注,如何在室内空间数据管理中融入情境信息已经成为人们关注的焦点问题。针对这一问题,综合考虑几何、拓扑、情境3类信息,建立了一个情境相关的双层室内空间数据模型。在分析了经典的空间划分方法后,提出了室内空间细粒度划分方法,并给出其形式化定义;引入层次互补的思想来组织室内空间,采用本体的方法将情境信息加入到模型中,使该模型对室内空间的表达更加灵活;最后,通过实例分析和列举模型的优势说明了建模方法的可行性和有效性。     

Granular best match algorithm for context-aware computing systems

In order to be context-aware, a system or application should adapt its behaviour according to current context, acquired by various context provision mechanisms. After acquiring current context, this information should be matched against the previously defined context sets. In this paper, a granular best match algorithm dealing with the subjective, fuzzy, multi-granular and multi-dimensional characteristics of contextual information is introduced. The CAPRA – Context-Aware Personal Reminder Agent tool is used to show the applicability of the new context matching algorithm. The obtained outputs showed that proposed algorithm produces the results which are more sensitive to the user’s intention, and more adaptive to the aforementioned characteristics of the contextual information than the traditional exact match method.     

InCloud: a cloud-based middleware for vehicular infotainment systems

Smart vehicles form pervasive environment to enhance user experience through multimedia enabled infotainment systems. In order to realize effective infotainment system for vehicles, we need to have context-aware applications that use latest (live) information for enhanced user experience. Such latest information is abundantly available on the Internet due to explosive growth of Web 3.0, which can be accessed through wireless communication infrastructures such as VANETs and LTE. In this paper we propose a cloud-based middleware framework, InCloud, for vehicular infotainment application development. The proposed framework follows service oriented architecture in which data filtering and fusion functionalities are delegated to the cloud. Data filtering and fusion reduce the data flow over wireless link. Furthermore, because most of the processing is done on the cloud, the client becomes lightweight and loosely coupled with Internet resources and underlying platforms in vehicles. We also propose a class-based fusion method for combining information from multiple resources on the Internet. The efficacy of the proposed framework is validated by developing three infotainment applications for vehicles: context-aware music, news, and an enhanced Direction (eDirection) application.

     

Managing context information in mobile devices

We present a uniform mobile terminal software framework that provides systematic methods for acquiring and processing useful context information from a user's surroundings and giving it to applications. The framework simplifies the development of context-aware mobile applications by managing raw context information gained from multiple sources and enabling higher-level context abstractions.     

Programmable context awareness framework

Context-awareness enables applications to provide end-users with a richer experience by enhancing their interactions with contextual information. Several frameworks have already been proposed to simplify the development of context-aware applications. These frameworks are focused on provisioning context data and on providing common semantics, definitions and representations of these context data. They assume that applications share the same semantic, which limits the range of use cases where a framework can be used, as that assumption induces a strong coupling between context management and application logic. This article proposes a framework that decouples context management from application business logic. The aim is to reduce the overhead on applications that run on resource-limited devices while still providing mechanisms to support context-awareness and behavior adaptation. The article presents an innovative approach that involves third-parties in context processing definition by structuring it using atomic functions. These functions can be designed by third-party developers using an XML-based programming language. Its implementation and evaluation demonstrates the benefits, in terms of flexibility, of using proven design patterns from software engineering for developing context-aware application.     

A JESS-enabled context elicitation system for providing context-aware Web services

Providing context-aware Web services is an adaptive process of delivering contextually matched Web services to meet service requesters’ needs. We define the term “context” from two perspectives: one from service requesters; and the other from Web services. From the former perspective, context is defined as the surrounding environment affecting requesters’ services discovery and access, such as requesters’ preferences, locations, activities, and accessible network and devices. From the latter perspective, context is defined as the surrounding environment affecting Web services delivery and execution, such as networks and protocols for service binding, devices and platforms for service execution, and so on. This paper presents a Java Expert System Shell (JESS)-enabled context elicitation system featuring an ontology-based context model that formally describes and acquires contextual information pertaining to service requesters and Web services. Based on the context elicitation system, we present a context-aware services-oriented architecture for providing context-aware Web service request, publication, and discovery. Implementation details of the context elicitation system and the evaluation results of context-aware service provision are also reported.     

Patterns for context-based knowledge fusion in decision support systems

The here presented research focuses on the context-based knowledge fusion patterns. Patterns are discovered based on an analysis and investigation of knowledge fusion processes in a context aware decision support system at the operational stage of the system functioning. At this stage the context-based knowledge fusion processes are manifested around the context. The patterns are generalized in regard to the following three aspects: (1) the effects that the knowledge fusion processes produce in the system; (2) the preservation of internal structures for the context and multiple sources the information/knowledge is fused from; and (3) the preservation of multiple sources and the context autonomies. At that, seven knowledge fusion patterns have been discovered: simple fusion, extension, instantiated fusion, configured fusion, adaptation, flat fusion, and historical fusion.     

面向普适环境的上下文感知中间件研究

针对普适环境下上下文感知计算需求,引入广义模型化理论,建立了一种面向通用环境资源的上下文信息数据模型;在此基础上,提出了上下文感知中间件体系框架,并详细阐述了其构件化的实施方案。该中间件平台的上下文获取层能够封装各类感知器捕获的资源信息,中间处理层负责信息的管理、推理和聚合,基于门面模式的上下文访问层提供同步和异步相结合的上下文信息统一访问入口。通过实验测试了平台的时间损耗,表明该中间件可提供通用的上下文感知服务且具有较好的系统性能。     

Context-aware system for proactive personalized service based on context history

Predicting the preferences of users and providing the personalized services or products based on their preferences are the important issues. However, the research considering users’ preferences on context-aware computing is a relatively insufficient research field. Hence, this paper aims to propose an agent-based framework for providing the personalized services using context history on context-aware computing. Based on the proposed framework, we implement a prototype system to show the feasibility of the framework. Previous researches require that the users input their preference manually, but this research provides the personalized services extracting the relationship between users’ profile and services under the same context automatically.